Neural Network Middle-Term Probabilistic Forecasting of Daily Power Consumption

TL;DR

This paper introduces a neural network-based probabilistic forecasting model for daily power consumption over a middle-term horizon, effectively incorporating trend, seasonality, and weather data to improve density forecasts.

Contribution

It presents a novel shallow neural network approach with autoregressive features for probabilistic power consumption forecasting, validated on a real dataset with superior results.

Findings

Achieved high-quality density forecasts for one-year ahead predictions.

Outperformed standard models in probabilistic accuracy measures.

Validated effectiveness using sector-relevant evaluation metrics.

Abstract

Middle-term horizon (months to a year) power consumption prediction is a main challenge in the energy sector, in particular when probabilistic forecasting is considered. We propose a new modelling approach that incorporates trend, seasonality and weather conditions, as explicative variables in a shallow Neural Network with an autoregressive feature. We obtain excellent results for density forecast on the one-year test set applying it to the daily power consumption in New England U.S.A.. The quality of the achieved power consumption probabilistic forecasting has been verified, on the one hand, comparing the results to other standard models for density forecasting and, on the other hand, considering measures that are frequently used in the energy sector as pinball loss and CI backtesting.